SST: A Simplified Swin Transformer-based Model for Taxi Destination Prediction based on Existing Trajectory
This work addresses trajectory prediction for intelligent location-based services, but it is incremental as it adapts an existing architecture to a specific domain.
The paper tackles taxi destination prediction by proposing a simplified Swin Transformer (SST) model that removes the shifted window mechanism to better handle consecutive trajectory data, achieving higher accuracy than state-of-the-art methods in experiments with real trajectory data.
Accurately predicting the destination of taxi trajectories can have various benefits for intelligent location-based services. One potential method to accomplish this prediction is by converting the taxi trajectory into a two-dimensional grid and using computer vision techniques. While the Swin Transformer is an innovative computer vision architecture with demonstrated success in vision downstream tasks, it is not commonly used to solve real-world trajectory problems. In this paper, we propose a simplified Swin Transformer (SST) structure that does not use the shifted window idea in the traditional Swin Transformer, as trajectory data is consecutive in nature. Our comprehensive experiments, based on real trajectory data, demonstrate that SST can achieve higher accuracy compared to state-of-the-art methods.